Search Results (26,638)

In heavy-haul railway systems, effective empty railcar distribution (ERD) can optimize composition planning and meet empty railcar requirements (ERRs) at all loading ends, thereby improving the efficiency of train operations. To solve practical challenges such as the imbalanced supply–demand of empty trains, redundant loading and unloading cycles, and prolonged waiting times, this study establishes a multi-objective and 0-1 integer programming model for ERD at the loading end of a heavy-haul railway. The model can simultaneously maximize the fulfilment of all ERRs, minimize the ERD delay time, and reduce the waiting time in the heavy-train combination problem under complex constraints, including the passing capacity of sections, combination capacity of stations, and ERR at the loading end. While traditional optimization methods such as mathematical programming or heuristic algorithms partially address these issues, they are ineffective under dynamic constraints and state-space explosion. Furthermore, traditional reinforcement learning-based methods, such as Q-learning, exhibit limitations in railway scheduling due to the state-space explosion problem and inadequate model generalization. To overcome these limitations, this study proposes an innovative framework; the ERD at the loading end of the heavy-haul railway is formalized as a Markov decision process and optimized using deep Q-network (DQN) reinforcement learning. In addition, this study proposes an experience data fusion mechanism that integrates the empirical rules of the dispatchers through a modular architecture, achieving real-time constraint compliance while maintaining scalability for practical implementation. The NSGA-II genetic algorithm for multi-objective problems is used in this study to evaluate the performance of the DQN algorithm. The experimental results demonstrate that the DQN algorithm can fully meet ERRs with zero delay and produce optimal schemes for train combinations. Meanwhile, NSGA-II presents superior performance in minimizing the combination waiting time and same-destination train combinations. Meanwhile, the DQN algorithm can identify superior ERD strategies in the expanded-action and state spaces, enabling the effective handling of complex constraint-based ERD. Full article

Table olive processing comprises multiple stages in which physical, chemical, and biological hazards may occur. Although risk assessment is a core element of Hazard Analysis and Critical Control Points (HACCP) systems, the selection of assessment tools remains insufficiently standardized. This study compared a 4 × 4 risk matrix and Failure Mode and Effects Analysis (FMEA) for hazard evaluation in Spanish-style and Californian-style table olive processing. Hazards were assessed across 41 processing stages for Spanish-style olives and selected key stages for Californian-style olives using probability × severity in the 4 × 4 matrix and severity × occurrence × detection in FMEA. Significant hazards were further evaluated using the Codex Alimentarius decision tree to identify critical control points (CCPs) and strengthened prerequisite programs (PRPs). Both tools identified similar significant hazards, including biological hazards associated with fermentation, brine management, storage, container sealing, and heat treatment, as well as physical hazards from foreign bodies and chemical hazards related to heavy metals, pesticide residues, mycotoxins, and food-contact material migration. FMEA provided greater analytical detail through the detection parameter, whereas the 4 × 4 matrix was simpler and more practical for complex flow diagrams. Overall, both tools were suitable for HACCP-based risk assessment in table olive processing. Full article

Green bonds have become an important financial instrument for supporting environmental investment and industrial transformation. This paper examines short-term profitability dynamics around first green bond issuance among heavy-polluting firms listed on China’s A-share market. Using a staggered-adoption framework based on the group-time average treatment effect estimator of Callaway and Sant’Anna we compare issuing firms after issuance with never-issuing and not-yet-issuing firms while controlling for firm characteristics, firm fixed effects, and year fixed effects. The estimates show that issuing firms experience an average post-issuance ROE decline of approximately 4.9 percentage points during the four years following issuance. Given that the average ROE in the sample is 0.0702, this estimate is economically substantial. Because green bond issuance is a voluntary corporate financing decision rather than an externally assigned policy shock, the estimates are interpreted as treatment-on-the-treated effects under the assumptions of no anticipation, overlap, and conditional parallel trends. Additional diagnostics and a DuPont-style mechanism analysis suggest that the post-issuance ROE decline is mainly associated with lower net profit margins and, to a lesser extent, lower asset turnover. Heterogeneity analyses indicate that the post-issuance profitability pressure varies across ownership types, regions, and industries. Full article

Coronary artery disease (CAD) remains a leading cause of global morbidity and mortality. Accurate diagnosis of ischemia-causing coronary stenoses is essential for guiding revascularization and improving outcomes. Although invasive fractional flow reserve (FFR) remains the gold standard for functional lesion assessment, its use is limited by procedural invasiveness, cost, and complexity. CT-derived FFR (FFRct), based on computational fluid dynamics (CFD), was the first major advance in noninvasive physiological assessment, but its adoption has been hindered by intensive off-site computation and dependence on high-quality imaging. This review summarizes the evolution from invasive FFR to AI-driven functional assessment of coronary lesions. We examine the principles and validation of CFD-based FFRct and then focus on the shift toward artificial intelligence, including both machine learning (ML) and deep learning (DL) approaches. These methods range from models using engineered geometric and plaque features trained on large synthetic datasets to end-to-end systems that learn directly from imaging data. We discuss key validation studies evaluating diagnostic accuracy, prognostic value, and clinical utility, with attention to performance in challenging settings such as intermediate stenoses, heavy calcification, and patients with comorbidities. We also highlight major barriers to widespread adoption, including dependence on input data quality, limited explainability, regulatory hurdles, and integration into clinical workflows. Finally, we outline future directions, including AI-enabled virtual PCI planning, multimodal risk stratification, and broader access to functional cardiac assessment. AI has the potential to transform noninvasive coronary imaging by enabling a single CCTA scan to provide rapid, integrated evaluation of anatomy, plaque characteristics, and physiological significance, supporting more personalized care and better clinical outcomes. Full article

We examine the growth of Internet Protocol version 6 (IPv6) routing state from 2010 to 2025. The global IPv4 address space has been exhausted, and the transition to IPv6 is ongoing. Using publicly accessible data from the RIPE Route Collectors (RRCs), we show that growth in the number of globally visible IPv6 routing prefixes follows different models over time, reflecting different growth patterns: exponential, power-law, and stretched-exponential. In addition to building models using publicly available RIPE data, we use this data source to demonstrate that our analysis holds across different Internet Exchange Points (IXPs) around the world and has predictive value. We provide in-depth analyses of IPv6 routing state growth, and we believe these are the first such analyses. Additionally, we highlight previous similar analyses of other aspects of network characteristics (such as topology and network traffic), and show that our analyses provide new insights. Specifically, we show the following: (1) previous models that have worked well for other network characteristics do not work well for routing state; (2) growth patterns for IPv6 routing state have changed significantly over time; (3) growth patterns cannot be described by a single model, and need to be analysed in a piecewise fashion; (4) fitting of previous data might not necessarily result in good predictive quality, and we identify the factors that may affect the predictive quality of a model and the predictive models that are suitable at the current time. Our analyses include metrics for assessing model fit. Overall, we observe a decrease in the rate of growth of IPv6 routing state, while the overall use of IPv6 continues to grow. We provide a critical evaluation of our approach, and also discuss possible factors affecting the growth of global IPv6 routing state. Full article

The Pad Bench Press (PBP) is a variation of the traditional bench press used by elite throwers to meet the mechanical demands of explosive upper-body actions in throwing events. The exercise involves a deliberately rapid eccentric phase, where the athlete allows the barbell to descend at high velocity, producing a rebound effect upon impact with the pad. This technique requires years of practice and is typically introduced early in an athlete’s development and refined progressively. The PBP is commonly used during maximal strength and power phases to provide a high-intensity, velocity-specific stimulus with heavy loads. This descriptive and exploratory case study presents a kinematic analysis of two internationally competitive Spanish shot putters, each with over 15 years of experience using the PBP. Barbell velocity data were obtained via 2D video analysis across multiple loads. The descriptive data indicate that, relative to the traditional bench press profiles reported in the literature, the PBP is associated with substantially stable peak velocities and markedly reduced sticking region, particularly at heavy loads. These findings provide a preliminary kinematic characterization of the PBP and suggest that it may offer a mechanically distinct stimulus compared to the traditional bench press, warranting further controlled investigation. Full article

Quantifying the impact of land use changes on the threat of flash-floods is a critical consideration in flood hazard planning and risk reduction, and is an area of active research. Here, a coupled Weather Research and Forecasting model hydrological extension package (i.e., WRF-Hydro) modeling approach is applied to simulate flash-flooding processes for short-duration, localized, intense precipitation events. To better understand the effect of urbanization on flash floods, a series of numerical experiments is performed surrounding Ellicott City, Maryland, a location which has experienced both significant heavy rainfall events and suburban development over the past several decades. Two intense rainfall events occurring on 30 July 2016 and 27 May 2018 are investigated, respectively, to first calibrate the hydrologic model performance and then quantify the sensitivity of flash flooding to varying degrees of urbanization. Performing the same experiments using observed historical land use states is of more limited insight, as the thrust of suburban development in the Ellicott City region significantly predates satellite-derived land use datasets. Results confirm that urbanization produces larger river streamflow, higher water stages, faster hydrologic responses to achieve peak flow discharge, and shorter recession limbs, even for very intense, short-duration events. The collective findings suggest that WRF-Hydro is applicable for both watershed flash flood prediction and hypothesis testing, and demonstrates potential utility to urban development decision-makers in locations such as Ellicott City, which could face future increases in catastrophic flooding. Full article

Lubricating oil monitoring provides continuous health information for early fault warning and maintenance decision-making in industrial gas turbines. However, real-world multi-sensor monitoring streams exhibit pronounced nonstationary thermodynamic drifts that often obscure subtle high-frequency residuals containing critical incipient degradation signatures. Prevailing data-driven monitoring models typically struggle to separate these macroscopic trends from stochastic wear-related fluctuations, and their restrictive distributional assumptions are often inadequate for the heteroscedastic and heavy-tailed nature of industrial residuals. To address these challenges, this study proposes ResAD-Net, a framework for early fault warning in nonstationary multi-sensor oil monitoring that combines trend–residual decoupling, trend-conditioned residual modeling, and residual-domain dependency learning. Specifically, a signal trend–residual decoupling strategy is adopted to separate slowly varying operational trends from stochastic residual fluctuations captured by the sensors, thereby exposing residual information that is more sensitive to incipient degradation. On this basis, a trend-conditioned diffusion model is introduced to characterize state-dependent, skewed residual distributions and generate residual sample ensembles for nonstationary monitoring. Meanwhile, a graph-based variational autoencoder is employed to learn latent intersensor dependency structures from the residual domain, providing diagnostic cues for temporal risk evolution analysis and sensor-level inspection. Experiments on a real-world industrial oil-monitoring record show that the proposed framework achieves an average F1-score of 0.985 with no observed false positives in the predefined pre-alarm reference interval of the finite test set. In addition to accurate anomaly detection, ResAD-Net captures early residual distributional shifts before clear macroscopic deviations emerge and provides diagnostic association cues for interpreting oil-monitoring changes around the system-level alarm. Full article

Urban low-altitude unmanned aerial vehicle (UAV) logistics networks face critical operational bottlenecks due to complex three-dimensional spatial blockages, continuous communication diffraction, and severe vulnerability to information-layer threats such as Automatic Dependent Surveillance—Broadcast (ADS-B) signal anomalies. To address these interconnected challenges, this paper proposes an event-driven, cloud–edge collaborative digital twin framework to guarantee continuous multi-link communication and flight safety. The architecture operates through a dual-tier “Teacher–Student” paradigm. Under secure conditions, a cloud digital twin acts as a high-capacity “Teacher,” employing Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to partition heterogeneous user topologies. It then utilizes an energy-guided stochastic diffusion sampling (EGSDS) method to refine initial macroscopic routing, generating precise, outage-free global trajectories by systematically minimizing non-line-of-sight (NLoS) observation penalties and kinematic regularization costs. To counteract signal anomalies, a distributed Time Difference of Arrival (TDOA) anchor network continuously validates UAV coordinate integrity. If a threshold is breached, control authority is instantly transferred to the UAV’s edge digital twin. This resource-constrained edge tier relies on a localized “Student” network trained via progressive distillation. By compressing the computationally heavy iterative diffusion process into a rapid one-step inference model, the UAV autonomously generates a secure, short-range emergency path that strictly adheres to minimum communication thresholds. Once interference clears, the cloud seamlessly regains control to complete the logistics mission. Experimental results demonstrate that the proposed scheme significantly outperforms conventional heuristic routing methods in cloud-based scenarios. Furthermore, the edge-based distillation mechanism substantially improves the overall trajectory survival rate under signal anomalies, ensuring resilient and continuous logistics operations. Full article

Mexico’s rice sector has long been characterized by declining cultivated area and heavy dependence on imports, raising concerns about food security and vulnerability to external shocks. In 2019, the federal government reintroduced price support through the Guaranteed Price Program for Basic Staples (PPGPB) to stabilize producer incomes and stimulate domestic rice production. This study provides the first empirical ex post evaluation of the PPGPB for rice during 2019–2021. Results show that paddy rice production increased by 29% in 2020 relative to the preceding decadal average. This increase was driven primarily by a 17.6% expansion in cultivated area, while yields increased by nearly 10%, indicating a predominantly extensive supply response. Econometric estimates suggest that the observed response exceeded both simulation-based predictions and the range of short-run elasticities commonly reported in the international literature. The results are consistent with the hypothesis that program effectiveness depended not only on price incentives, but also on expanded incentive coverage and program redesign introduced in 2020. While guaranteed price policies may contribute to short-run recovery in structurally weakened staple-crop sectors, they are unlikely to be sufficient to achieve the production objectives established under Plan México 2025–2030. The findings underscore the need for complementary structural investments. Full article

This study focuses on the design and development of the Double Purpose Bench Vise to address safety, efficiency, and adaptability challenges in welding and fabrication environments. The project responds to limitations of conventional vises that restrict precision and increase the risk of strain-related injuries when handling heavy, irregular, or vertically oriented workpieces. Through an engineering-based development approach involving analysis, design, fabrication, and performance evaluation, the study introduces a Double Jaw Vertical Bench Vise equipped with a dual-clamping system and an integrated hydraulic jack mechanism for precise vertical adjustment with minimal physical effort. The device is designed to securely hold various materials, including metal bars, pipes, and wooden components, during cutting, grinding, shaping, welding, and assembly operations. Evaluation results from functional testing and user feedback indicate improved clamping stability, alignment accuracy, and ergonomic performance compared to traditional models, although refinements in structural optimization, weight distribution, and user interface components are recommended. The study suggests further prototype enhancement, extended field testing, and integration of advanced ergonomic and safety features to maximize durability, usability, and overall productivity in professional workshops and technical training laboratories. Full article

Aiming at the problems of glare interference, local overexposure and detail loss caused by artificial light sources such as vehicle lamps and street lamps in nighttime road scenes, as well as the challenges of existing glare suppression models with large parameters, high computational complexity and difficulty in deploying on edge devices, this paper proposes a lightweight glare suppression network (LGSNet) based on ghost depthwise separable convolution and Lightweight Parallel Attention. Based on the U-Net architecture, the network introduces ghost depthwise separable convolution blocks (GhostDSC) in the encoder and decoder, which generates ghost features through cheap linear transformations by exploiting feature map redundancy, significantly reducing model parameters and computational costs while maintaining feature representation ability. Meanwhile, a Lightweight Parallel Attention (LPA) module is designed in the decoder stage, which integrates channel attention and pixel attention in parallel, enhancing the network’s attention to glare regions and edge details with extremely low parameter increment to improve detail recovery accuracy. In addition, a joint loss function consisting of background loss, glare loss and reconstruction loss is constructed to collaboratively optimize glare suppression and detail preservation. Experimental results on the public Flare7K++ dataset and the self-built nighttime road glare dataset NRGD show that the proposed method has only 7.45 M parameters, much lower than standard U-Net and Uformer. It achieves competitive results on full-reference metrics such as PSNR, SSIM, LPIPS and no-reference metrics such as NIQE, BRISQUE, PIQE, and can effectively suppress various types of glare interference and restore obscured scene details. It achieves a superior trade-off between model complexity and enhancement performance, significantly reducing the parameter count and computational overhead compared to heavy baselines, thereby offering a highly efficient solution for resource-aware glare suppression tasks. Full article

The production of inexpensive, effective sorbents from natural materials for the purification of water bodies and/or soils is a pressing problem. Therefore, the purpose of this manuscript is to summarize current approaches to the use of brown coal (lignite) and its processing products (humic acids, HAs) as sorbents for the purification of aqueous and soil environments from heavy metal ions and other pollutants. Modification of lignite (chemical, biological, physicochemical) or the creation of lignite–mineral composites significantly increases its sorption capacity and stability: after modification, the sorption capacity can reach more than 85 mg of heavy metals per g of sorbent, which is only 3 times lower than that of specialized, expensive sorbents. Also, good results are achieved in the case of sorption of water-soluble organic drugs, dyes, etc. Humic acids obtained from brown coal have better selectivity and efficiency than the original lignite, and slightly worse than the modified one, in terms of removing cadmium, lead, copper, and other toxic elements; and also, can complex with organic xenobiotics. Current research trends indicate growing interest in multifunctional composite sorbents, environmentally friendly extraction technologies, and the development of materials with enhanced selectivity and regeneration ability. Future studies should focus on improving the understanding of sorption mechanisms, optimizing modification strategies, scaling up lignite-based technologies for practical environmental applications, and developing waste-free technologies to produce sorbents from lignite. Full article

Population-based evidence on sleep quality in Kazakhstan remains limited. This study describes sleep quality as a multidimensional construct among adults in Kazakhstan using data collected during the first national survey wave after the adoption of a single national time zone. The survey was designed as a national post-transition baseline assessment and not as an evaluation of the causal impact of the time-zone reform. Associations with socio-demographic, behavioural, clinical, and regional factors were examined. We conducted a nationally representative cross-sectional survey of adults aged 18–69 years in Kazakhstan from May to October 2025 using a multistage stratified cluster design. Sleep quality was assessed with the Pittsburgh Sleep Quality Index (PSQI). Poor sleep quality was defined as a global PSQI score > 5. Complete PSQI data were available for 5872 participants. Descriptive analyses examined the global PSQI score and the seven component scores. Survey-weighted multivariable logistic regression was used to identify factors independently associated with poor sleep quality. The weighted prevalence of poor sleep quality was 28.1%, and the weighted mean global PSQI score was 4.43. The greatest component burden was attributable to sleep latency (mean 0.87), subjective sleep quality (0.82), and sleep disturbances (0.80), whereas use of sleep medication contributed minimally (0.11). Poor sleep quality was more common among women, older adults, urban residents, and participants with diabetes, current smoking, heavy episodic drinking, and depressive symptoms. In the adjusted model, female sex (aOR 1.37, 95% CI 1.19–1.57), age 55 years or older versus 18–24 years (1.98, 1.53–2.55), diabetes (1.47, 1.22–1.78), current smoking (1.28, 1.10–1.50), heavy episodic drinking (1.43, 1.16–1.76), and depressive symptoms (4.26, 3.52–5.15) were independently associated with higher odds of poor sleep quality. Rural residence was inversely associated with the outcome (0.71, 0.61–0.84). Compared with the North, higher odds were observed in the Central region (2.00, 1.46–2.74), East (1.94, 1.48–2.53), West (1.48, 1.17–1.88), and Almaty city (2.18, 1.72–2.76). Poor sleep quality is common among adults in Kazakhstan and is characterized primarily by difficulties with sleep initiation, perceived sleep quality, and nocturnal disturbances. The findings provide national post-transition baseline evidence and suggest that sleep health surveillance in Kazakhstan should prioritize demographic, mental health, behavioural, and regional inequalities while avoiding causal interpretation of the time-zone reform itself. Full article

Worldwide, flash floods are among the most unpredictable and hazardous hydrological phenomena, particularly in arid and semi-arid regions such as the Kingdom of Saudi Arabia, where sudden heavy rainfall follows prolonged periods of drought. This work presents an effective integrated model for flood hazard evaluation in the Hanefah Catchment, a socioeconomically vital area in the central part of Saudi Arabia that includes the capital city, Riyadh. Using high-resolution ALOS PALSAR 12.5 m Digital Elevation Model spatial data, we extracted and investigated indicative linear, areal, and relief morphometric keys of 64 sub-catchments. This paper employs a dual-method concept that integrates a multi-criteria ranking method and the El-Shamy approach in conjunction with morphotectonic analysis to model flood-susceptibility zones. Furthermore, this paper suggests a comparative assessment of low-cost morphometric models under data-scarce conditions, assessing the multi-criteria ranking method against El-Shamy’s approach, using the topographic position index (TPI) as an internal terrain scale benchmark. The ranking method successfully assigned 85.7% of the historically recorded flood locations to the high-hazard zone that covers ~24.22% of the Hanefah catchment. In contrast, the El-Shamy approach systematically underestimated flood susceptibility because regional tectonic activity increases bifurcation ratios, resulting in just ~42.9% of the historical floods being assigned to the high-hazard zone. The final results highlight the northern and northwestern parts of the catchment as high-hazard zones, characterized by high drainage density and steep relief. This study provides a refined, cost-effective model that aligns with the strategic objectives of Saudi Vision 2030 for sustainable water resources management and significant urban development. Full article